Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a critical issue known as the data sparsity problem, which greatly limits their performance. Several approaches have been proposed in the literature to tackle the problem of data sparsity, among which cross-domain collaborative filtering (CDCF) has gained significant attention in the recent past. In order to compensate for the scarcity of available feedback in a target domain, the CDCF approach makes use of information available in other auxiliary domains. Most of the traditional CDCF approach aim is to find a common set of entities (users or items) across the domains and then use them as a bridge for knowledge transfer. However, most real-world datasets are collected from different domains, so they often lack information about anchor points or reference information for entity alignment. In this paper, we propose a domain adaptation technique to align the embeddings of users and items across the two domains. Our approach first exploits the available textual and visual information to independently learn a multi-view latent representation for each user and item in the auxiliary and target domains. The different representations of a user or item are then fused to generate the corresponding unified representation. A domain classifier is then trained to learn the embedding for the domain alignment by fixing the unified features as the anchor points. Experiments on two publicly benchmark datasets indicate the effectiveness of our proposed approach.

翻译：协同过滤（CF）已成为构建推荐系统最常用的实现策略之一，其核心思想是利用个体的使用模式来生成个性化推荐。CF技术，尤其是针对新上线平台时，常面临一个关键问题——数据稀疏性，这极大地限制了其性能。文献中已提出多种方法来解决数据稀疏问题，其中跨域协同过滤（CDCF）近年来受到了广泛关注。为弥补目标域中可用反馈的不足，CDCF方法利用了其他辅助域中的信息。大多数传统CDCF方法的目标是在域间寻找共同的实体集（用户或项目），并将其作为知识迁移的桥梁。然而，实际数据集通常来自不同域，往往缺乏锚点信息或实体对齐的参考信息。本文提出了一种域适应技术，用于对齐两个域中用户和项目的嵌入表示。我们的方法首先利用可用的文本和视觉信息，在辅助域和目标域中分别为每个用户和项目独立学习多视图潜在表示，然后将用户或项目的不同表示融合以生成相应的统一表示。接着，通过将统一特征固定为锚点，训练一个域分类器来学习用于域对齐的嵌入表示。在两个公开基准数据集上的实验表明了我们所提出方法的有效性。